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Communicating data quality to your business executives in an effective and interactive way, is one of the most important key tasks you have on hand. 1 million points of data in a 90-minute presentation may not mean anything to an audience who has zero knowledge in data-quality practices. You need to make it easy for them to understand what you are saying without going into all the technical jargon or confusing mathematics that might discourage them from caring.
In data warehousing, which enables corporate data to be collected in one location, the only way to avoid being inaccurate is to make sure that data quality is constantly monitored. This blog will give examples of technologies used for time series anomaly detection and furthermore outline how they can be used for different managerial situations.
Outlier detection and drift detection are two popular approaches in data analytics. Outlier detection is a powerful method that helps identify unusual data points, whereas drift detection detects gradual changes in a time series. In this article, you'll learn about the differences between outlier and drift detection, their applications and working principles, as well as some benefits of each approach.
A data quality analysis is a systematic way to evaluate the accuracy of your data and identify, correct, or remove any inaccuracies. Although this process might sound straightforward, there are many things that can go wrong with it - inconsistent data processes, lack of employee knowledge about accurate data standards, etc. Learn how Unit Testing for Data Quality can help you avoid these problems and create more accurate data!
Data drift is the one of key factors in determining the success of prediction models. Data drift can lead to erroneous predictions and as a result, it may affect business outcomes. Learn more about data drift and how to detect it in this blog post.
